identification of presymptomatic NF2 mutation carriers by DNA diagnosis permits improved genetic counselling and clinical management in at-risk subjects. The early detection of VS by gadolinium-enhanced
{"title":"TUBE","authors":"Daheng Wang, Tianwen Jiang, N. Chawla, Meng Jiang","doi":"10.1145/3292500.3330867","DOIUrl":"https://doi.org/10.1145/3292500.3330867","url":null,"abstract":"identification of presymptomatic NF2 mutation carriers by DNA diagnosis permits improved genetic counselling and clinical management in at-risk subjects. The early detection of VS by gadolinium-enhanced","PeriodicalId":186134,"journal":{"name":"Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining","volume":"7 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-07-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132293455","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Xiaoli Tang, Tengyun Wang, Haizhi Yang, Hengjie Song
Recently, much attention has been paid to the usage of knowledge graph within the context of recommender systems to alleviate the data sparsity and cold-start problems. However, when incorporating entities from a knowledge graph to represent users, most existing works are unaware of the relationships between these entities and users. As a result, the recommendation results may suffer a lot from some unrelated entities. In this paper, we investigate how to explore these relationships which are essentially determined by the interactions among entities. Firstly, we categorize the interactions among entities into two types: inter-entity-interaction and intra-entity-interaction. Inter-entity-interaction is the interactions among entities that affect their importances to represent users. And intra-entity-interaction is the interactions within an entity that describe the different characteristics of this entity when involved in different relations. Then, considering these two types of interactions, we propose a novel model named Attention-enhanced Knowledge-aware User Preference Model (AKUPM) for click-through rate (CTR) prediction. More specifically, a self-attention network is utilized to capture the inter-entity-interaction by learning appropriate importance of each entity w.r.t the user. Moreover, the intra-entity-interaction is modeled by projecting each entity into its connected relation spaces to obtain the suitable characteristics. By doing so, AKUPM is able to figure out the most related part of incorporated entities (i.e., filter out the unrelated entities). Extensive experiments on two real-world public datasets demonstrate that AKUPM achieves substantial gains in terms of common evaluation metrics (e.g., AUC, ACC and Recall@top-K) over several state-of-the-art baselines.
{"title":"AKUPM","authors":"Xiaoli Tang, Tengyun Wang, Haizhi Yang, Hengjie Song","doi":"10.1145/3292500.3330705","DOIUrl":"https://doi.org/10.1145/3292500.3330705","url":null,"abstract":"Recently, much attention has been paid to the usage of knowledge graph within the context of recommender systems to alleviate the data sparsity and cold-start problems. However, when incorporating entities from a knowledge graph to represent users, most existing works are unaware of the relationships between these entities and users. As a result, the recommendation results may suffer a lot from some unrelated entities. In this paper, we investigate how to explore these relationships which are essentially determined by the interactions among entities. Firstly, we categorize the interactions among entities into two types: inter-entity-interaction and intra-entity-interaction. Inter-entity-interaction is the interactions among entities that affect their importances to represent users. And intra-entity-interaction is the interactions within an entity that describe the different characteristics of this entity when involved in different relations. Then, considering these two types of interactions, we propose a novel model named Attention-enhanced Knowledge-aware User Preference Model (AKUPM) for click-through rate (CTR) prediction. More specifically, a self-attention network is utilized to capture the inter-entity-interaction by learning appropriate importance of each entity w.r.t the user. Moreover, the intra-entity-interaction is modeled by projecting each entity into its connected relation spaces to obtain the suitable characteristics. By doing so, AKUPM is able to figure out the most related part of incorporated entities (i.e., filter out the unrelated entities). Extensive experiments on two real-world public datasets demonstrate that AKUPM achieves substantial gains in terms of common evaluation metrics (e.g., AUC, ACC and Recall@top-K) over several state-of-the-art baselines.","PeriodicalId":186134,"journal":{"name":"Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining","volume":"129 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-07-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115580271","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
B. Adhikari, Xinfeng Xu, Naren Ramakrishnan, B. Prakash
Influenza leads to regular losses of lives annually and requires careful monitoring and control by health organizations. Annual influenza forecasts help policymakers implement effective countermeasures to control both seasonal and pandemic outbreaks. Existing forecasting techniques suffer from problems such as poor forecasting performance, lack of modeling flexibility, data sparsity, and/or lack of intepretability. We propose EpiDeep, a novel deep neural network approach for epidemic forecasting which tackles all of these issues by learning meaningful representations of incidence curves in a continuous feature space and accurately predicting future incidences, peak intensity, peak time, and onset of the upcoming season. We present extensive experiments on forecasting ILI (influenza-like illnesses) in the United States, leveraging multiple metrics to quantify success. Our results demonstrate that EpiDeep is successful at learning meaningful embeddings and, more importantly, that these embeddings evolve as the season progresses. Furthermore, our approach outperforms non-trivial baselines by up to 40%.
{"title":"EpiDeep","authors":"B. Adhikari, Xinfeng Xu, Naren Ramakrishnan, B. Prakash","doi":"10.1145/3292500.3330917","DOIUrl":"https://doi.org/10.1145/3292500.3330917","url":null,"abstract":"Influenza leads to regular losses of lives annually and requires careful monitoring and control by health organizations. Annual influenza forecasts help policymakers implement effective countermeasures to control both seasonal and pandemic outbreaks. Existing forecasting techniques suffer from problems such as poor forecasting performance, lack of modeling flexibility, data sparsity, and/or lack of intepretability. We propose EpiDeep, a novel deep neural network approach for epidemic forecasting which tackles all of these issues by learning meaningful representations of incidence curves in a continuous feature space and accurately predicting future incidences, peak intensity, peak time, and onset of the upcoming season. We present extensive experiments on forecasting ILI (influenza-like illnesses) in the United States, leveraging multiple metrics to quantify success. Our results demonstrate that EpiDeep is successful at learning meaningful embeddings and, more importantly, that these embeddings evolve as the season progresses. Furthermore, our approach outperforms non-trivial baselines by up to 40%.","PeriodicalId":186134,"journal":{"name":"Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining","volume":"93 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-07-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115665580","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Eitam Sheetrit, N. Nissim, D. Klimov, Yuval Shahar
Sepsis is a condition caused by the body's overwhelming and life-threatening response to infection, which can lead to tissue damage, organ failure, and finally death. Today, sepsis is one of the leading causes of mortality among populations in intensive care units (ICUs). Sepsis is difficult to predict, diagnose, and treat, as it involves analyzing different sets of multivariate time-series, usually with problems of missing data, different sampling frequencies, and random noise. Here, we propose a new dynamic-behavior-based model, which we call a Temporal Probabilistic proFile (TPF), for classification and prediction tasks of multivariate time series. In the TPF method, the raw, time-stamped data are first abstracted into a series of higher-level, meaningful concepts, which hold over intervals characterizing time periods. We then discover frequently repeating temporal patterns within the data. Using the discovered patterns, we create a probabilistic distribution of the temporal patterns of the overall entity population, of each target class in it, and of each entity. We then exploit TPFs as meta-features to classify the time series of new entities, or to predict their outcome, by measuring their TPF distance, either to the aggregated TPF of each class, or to the individual TPFs of each of the entities, using negative cross entropy. Our experimental results on a large benchmark clinical data set show that TPFs improve sepsis prediction capabilities, and perform better than other machine learning approaches.
{"title":"Temporal Probabilistic Profiles for Sepsis Prediction in the ICU","authors":"Eitam Sheetrit, N. Nissim, D. Klimov, Yuval Shahar","doi":"10.1145/3292500.3330747","DOIUrl":"https://doi.org/10.1145/3292500.3330747","url":null,"abstract":"Sepsis is a condition caused by the body's overwhelming and life-threatening response to infection, which can lead to tissue damage, organ failure, and finally death. Today, sepsis is one of the leading causes of mortality among populations in intensive care units (ICUs). Sepsis is difficult to predict, diagnose, and treat, as it involves analyzing different sets of multivariate time-series, usually with problems of missing data, different sampling frequencies, and random noise. Here, we propose a new dynamic-behavior-based model, which we call a Temporal Probabilistic proFile (TPF), for classification and prediction tasks of multivariate time series. In the TPF method, the raw, time-stamped data are first abstracted into a series of higher-level, meaningful concepts, which hold over intervals characterizing time periods. We then discover frequently repeating temporal patterns within the data. Using the discovered patterns, we create a probabilistic distribution of the temporal patterns of the overall entity population, of each target class in it, and of each entity. We then exploit TPFs as meta-features to classify the time series of new entities, or to predict their outcome, by measuring their TPF distance, either to the aggregated TPF of each class, or to the individual TPFs of each of the entities, using negative cross entropy. Our experimental results on a large benchmark clinical data set show that TPFs improve sepsis prediction capabilities, and perform better than other machine learning approaches.","PeriodicalId":186134,"journal":{"name":"Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining","volume":"49 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-07-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114084858","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Peng Tian, Yuan Guo, Jayashree Kalpathy-Cramer, S. Ostmo, J. P. Campbell, M. Chiang, Jennifer G. Dy, Deniz Erdoğmuş, Stratis Ioannidis
Retinopathy of Prematurity (ROP) is a leading cause for childhood blindness worldwide. An automated ROP detection system could significantly improve the chance of a child receiving proper diagnosis and treatment. We propose a means of producing a continuous severity score in an automated fashion, regressed from both (a) diagnostic class labels as well as (b) comparison outcomes. Our generative model combines the two sources, and successfully addresses inherent variability in diagnostic outcomes. In particular, our method exhibits an excellent predictive performance of both diagnostic and comparison outcomes over a broad array of metrics, including AUC, precision, and recall.
{"title":"A Severity Score for Retinopathy of Prematurity","authors":"Peng Tian, Yuan Guo, Jayashree Kalpathy-Cramer, S. Ostmo, J. P. Campbell, M. Chiang, Jennifer G. Dy, Deniz Erdoğmuş, Stratis Ioannidis","doi":"10.1145/3292500.3330713","DOIUrl":"https://doi.org/10.1145/3292500.3330713","url":null,"abstract":"Retinopathy of Prematurity (ROP) is a leading cause for childhood blindness worldwide. An automated ROP detection system could significantly improve the chance of a child receiving proper diagnosis and treatment. We propose a means of producing a continuous severity score in an automated fashion, regressed from both (a) diagnostic class labels as well as (b) comparison outcomes. Our generative model combines the two sources, and successfully addresses inherent variability in diagnostic outcomes. In particular, our method exhibits an excellent predictive performance of both diagnostic and comparison outcomes over a broad array of metrics, including AUC, precision, and recall.","PeriodicalId":186134,"journal":{"name":"Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining","volume":"16 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-07-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114208987","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Seiya Tokui, Ryosuke Okuta, Takuya Akiba, Yusuke Niitani, Toru Ogawa, S. Saito, Shuji Suzuki, Kota Uenishi, Brian K. Vogel, Hiroyuki Yamazaki Vincent
Software frameworks for neural networks play a key role in the development and application of deep learning methods. In this paper, we introduce the Chainer framework, which intends to provide a flexible, intuitive, and high performance means of implementing the full range of deep learning models needed by researchers and practitioners. Chainer provides acceleration using Graphics Processing Units with a familiar NumPy-like API through CuPy, supports general and dynamic models in Python through Define-by-Run, and also provides add-on packages for state-of-the-art computer vision models as well as distributed training.
{"title":"Chainer: A Deep Learning Framework for Accelerating the Research Cycle","authors":"Seiya Tokui, Ryosuke Okuta, Takuya Akiba, Yusuke Niitani, Toru Ogawa, S. Saito, Shuji Suzuki, Kota Uenishi, Brian K. Vogel, Hiroyuki Yamazaki Vincent","doi":"10.1145/3292500.3330756","DOIUrl":"https://doi.org/10.1145/3292500.3330756","url":null,"abstract":"Software frameworks for neural networks play a key role in the development and application of deep learning methods. In this paper, we introduce the Chainer framework, which intends to provide a flexible, intuitive, and high performance means of implementing the full range of deep learning models needed by researchers and practitioners. Chainer provides acceleration using Graphics Processing Units with a familiar NumPy-like API through CuPy, supports general and dynamic models in Python through Define-by-Run, and also provides add-on packages for state-of-the-art computer vision models as well as distributed training.","PeriodicalId":186134,"journal":{"name":"Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining","volume":"36 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-07-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115852391","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Text embedding is a fundamental component for extracting text features in production-level data mining and machine learning systems given textual information is the most ubiqutious signals. However, practitioners often face the tradeoff between effectiveness of underlying embedding algorithms and cost of training and maintaining various embedding results in large-scale applications. In this paper, we propose a multitask text embedding solution called PinText for three major vertical surfaces including homefeed, related pins, and search in Pinterest, which consolidates existing text embedding algorithms into a single solution and produces state-of-the-art performance. Specifically, we learn word level semantic vectors by enforcing that the similarity between positive engagement pairs is larger than the similarity between a randomly sampled background pairs. Based on the learned semantic vectors, we derive embedding vector of a user, a pin, or a search query by simply averaging its word level vectors. In this common compact vector space, we are able to do unified nearest neighbor search with hashing by Hadoop jobs or dockerized images on Kubernetes cluster. Both offline evaluation and online experiments show effectiveness of this PinText system and save storage cost of multiple open-sourced embeddings significantly.
{"title":"PinText: A Multitask Text Embedding System in Pinterest","authors":"Jinfeng Zhuang, Yu Liu","doi":"10.1145/3292500.3330671","DOIUrl":"https://doi.org/10.1145/3292500.3330671","url":null,"abstract":"Text embedding is a fundamental component for extracting text features in production-level data mining and machine learning systems given textual information is the most ubiqutious signals. However, practitioners often face the tradeoff between effectiveness of underlying embedding algorithms and cost of training and maintaining various embedding results in large-scale applications. In this paper, we propose a multitask text embedding solution called PinText for three major vertical surfaces including homefeed, related pins, and search in Pinterest, which consolidates existing text embedding algorithms into a single solution and produces state-of-the-art performance. Specifically, we learn word level semantic vectors by enforcing that the similarity between positive engagement pairs is larger than the similarity between a randomly sampled background pairs. Based on the learned semantic vectors, we derive embedding vector of a user, a pin, or a search query by simply averaging its word level vectors. In this common compact vector space, we are able to do unified nearest neighbor search with hashing by Hadoop jobs or dockerized images on Kubernetes cluster. Both offline evaluation and online experiments show effectiveness of this PinText system and save storage cost of multiple open-sourced embeddings significantly.","PeriodicalId":186134,"journal":{"name":"Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining","volume":"665 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-07-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116100463","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
One of the most interesting application scenarios in anomaly detection is when sequential data are targeted. For example, in a safety-critical environment, it is crucial to have an automatic detection system to screen the streaming data gathered by monitoring sensors and to report abnormal observations if detected in real-time. Oftentimes, stakes are much higher when these potential anomalies are intentional or goal-oriented. We propose an end-to-end framework for sequential anomaly detection using inverse reinforcement learning (IRL), whose objective is to determine the decision-making agent's underlying function which triggers his/her behavior. The proposed method takes the sequence of actions of a target agent (and possibly other meta information) as input. The agent's normal behavior is then understood by the reward function which is inferred via IRL. We use a neural network to represent a reward function. Using a learned reward function, we evaluate whether a new observation from the target agent follows a normal pattern. In order to construct a reliable anomaly detection method and take into consideration the confidence of the predicted anomaly score, we adopt a Bayesian approach for IRL. The empirical study on publicly available real-world data shows that our proposed method is effective in identifying anomalies.
{"title":"Sequential Anomaly Detection using Inverse Reinforcement Learning","authors":"Min-hwan Oh, G. Iyengar","doi":"10.1145/3292500.3330932","DOIUrl":"https://doi.org/10.1145/3292500.3330932","url":null,"abstract":"One of the most interesting application scenarios in anomaly detection is when sequential data are targeted. For example, in a safety-critical environment, it is crucial to have an automatic detection system to screen the streaming data gathered by monitoring sensors and to report abnormal observations if detected in real-time. Oftentimes, stakes are much higher when these potential anomalies are intentional or goal-oriented. We propose an end-to-end framework for sequential anomaly detection using inverse reinforcement learning (IRL), whose objective is to determine the decision-making agent's underlying function which triggers his/her behavior. The proposed method takes the sequence of actions of a target agent (and possibly other meta information) as input. The agent's normal behavior is then understood by the reward function which is inferred via IRL. We use a neural network to represent a reward function. Using a learned reward function, we evaluate whether a new observation from the target agent follows a normal pattern. In order to construct a reliable anomaly detection method and take into consideration the confidence of the predicted anomaly score, we adopt a Bayesian approach for IRL. The empirical study on publicly available real-world data shows that our proposed method is effective in identifying anomalies.","PeriodicalId":186134,"journal":{"name":"Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining","volume":"61 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-07-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122070924","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Mateusz Fedoryszak, Brent Frederick, V. Rajaram, Changtao Zhong
Social networks are quickly becoming the primary medium for discussing what is happening around real-world events. The information that is generated on social platforms like Twitter can produce rich data streams for immediate insights into ongoing matters and the conversations around them. To tackle the problem of event detection, we model events as a list of clusters of trending entities over time. We describe a real-time system for discovering events that is modular in design and novel in scale and speed: it applies clustering on a large stream with millions of entities per minute and produces a dynamically updated set of events. In order to assess clustering methodologies, we build an evaluation dataset derived from a snapshot of the full Twitter Firehose and propose novel metrics for measuring clustering quality. Through experiments and system profiling, we highlight key results from the offline and online pipelines. Finally, we visualize a high profile event on Twitter to show the importance of modeling the evolution of events, especially those detected from social data streams.
{"title":"Real-time Event Detection on Social Data Streams","authors":"Mateusz Fedoryszak, Brent Frederick, V. Rajaram, Changtao Zhong","doi":"10.1145/3292500.3330689","DOIUrl":"https://doi.org/10.1145/3292500.3330689","url":null,"abstract":"Social networks are quickly becoming the primary medium for discussing what is happening around real-world events. The information that is generated on social platforms like Twitter can produce rich data streams for immediate insights into ongoing matters and the conversations around them. To tackle the problem of event detection, we model events as a list of clusters of trending entities over time. We describe a real-time system for discovering events that is modular in design and novel in scale and speed: it applies clustering on a large stream with millions of entities per minute and produces a dynamically updated set of events. In order to assess clustering methodologies, we build an evaluation dataset derived from a snapshot of the full Twitter Firehose and propose novel metrics for measuring clustering quality. Through experiments and system profiling, we highlight key results from the offline and online pipelines. Finally, we visualize a high profile event on Twitter to show the importance of modeling the evolution of events, especially those detected from social data streams.","PeriodicalId":186134,"journal":{"name":"Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining","volume":"20 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-07-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125280830","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Lucas Bernardi, Themistoklis Mavridis, PabloA . Estevez
Booking.com is the world's largest online travel agent where millions of guests find their accommodation and millions of accommodation providers list their properties including hotels, apartments, bed and breakfasts, guest houses, and more. During the last years we have applied Machine Learning to improve the experience of our customers and our business. While most of the Machine Learning literature focuses on the algorithmic or mathematical aspects of the field, not much has been published about how Machine Learning can deliver meaningful impact in an industrial environment where commercial gains are paramount. We conducted an analysis on about 150 successful customer facing applications of Machine Learning, developed by dozens of teams in Booking.com, exposed to hundreds of millions of users worldwide and validated through rigorous Randomized Controlled Trials. Following the phases of a Machine Learning project we describe our approach, the many challenges we found, and the lessons we learned while scaling up such a complex technology across our organization. Our main conclusion is that an iterative, hypothesis driven process, integrated with other disciplines was fundamental to build 150 successful products enabled by Machine Learning.
{"title":"150 Successful Machine Learning Models: 6 Lessons Learned at Booking.com","authors":"Lucas Bernardi, Themistoklis Mavridis, PabloA . Estevez","doi":"10.1145/3292500.3330744","DOIUrl":"https://doi.org/10.1145/3292500.3330744","url":null,"abstract":"Booking.com is the world's largest online travel agent where millions of guests find their accommodation and millions of accommodation providers list their properties including hotels, apartments, bed and breakfasts, guest houses, and more. During the last years we have applied Machine Learning to improve the experience of our customers and our business. While most of the Machine Learning literature focuses on the algorithmic or mathematical aspects of the field, not much has been published about how Machine Learning can deliver meaningful impact in an industrial environment where commercial gains are paramount. We conducted an analysis on about 150 successful customer facing applications of Machine Learning, developed by dozens of teams in Booking.com, exposed to hundreds of millions of users worldwide and validated through rigorous Randomized Controlled Trials. Following the phases of a Machine Learning project we describe our approach, the many challenges we found, and the lessons we learned while scaling up such a complex technology across our organization. Our main conclusion is that an iterative, hypothesis driven process, integrated with other disciplines was fundamental to build 150 successful products enabled by Machine Learning.","PeriodicalId":186134,"journal":{"name":"Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining","volume":"10 3 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-07-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126034289","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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